Prognostic Factors and Scoring Model for Survival in Metastatic Biliary ...

pISSN 1598-2998, eISSN 2005-9256 Cancer Res Treat. 2017 Feb 6 [Epub ahead of print]

https://doi.org/10.4143/crt.2016.538 Open Access

Original Article

Prognostic Factors and Scoring Model for Survival in Metastatic Biliary Tract Cancer

Purpose Metastatic biliary tract cancer (mBTC) has a dismal prognosis. In this study, an independent dataset of patients with mBTC was used to implement and validate a routine clinico-laboratory parameter-based scoring model for risk group identification.

Hyung Soon Park, MD, PhD1 Ji Soo Park, MD2 You Jin Chun, MD3 Yun Ho Roh, MS4 Jieun Moon, PhD4 Hong Jae Chon, MD5 Hye Jin Choi, MD, PhD1 Joon Seong Park, MD, PhD3 Dong Ki Lee, MD, PhD3 Se-Joon Lee, MD, PhD3 Dong Sup Yoon, MD, PhD3 Hei-Cheul Jeung, MD, PhD3,6

Materials and Methods From September 2006 to February 2015, 482 patients with mBTC were assigned randomly (ratio, 7:3) into investigational (n=340) and validation datasets (n=142). The continuous variables were dichotomized using a normal range or the best cutoff values determined using the Contal and O'Quigley statistical methods. Following a Cox’s proportional hazard model, the scoring model was derived by summing the rounded chi-square scores for the factors identified by multivariate analysis.

1 Division of Medical Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, 2Cancer Prevention Center, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, 3 Pancreatobiliary Cancer Clinic, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, 4Biostatistics Collaboration Unit, Medical Research Center, Yonsei University College of Medicine, Seoul, 5 Division of Medical Oncology, Department of Internal Medicine, CHA Bundang Medical Center, CHA University, Seongnam, 6 Songdang Institute for Cancer Research, Yonsei University College of Medicine, Seoul, Korea

Results The performance status (Eastern Cooperative Oncology Group 3-4), hypoalbuminemia (< 3.4 mg/dL), carcinoembryonic antigen (≥ 9 ng/mL), neutrophil-to-lymphocyte ratio (≥ 3.0), and carbohydrate antigen 19-9 (≥ 120 U/mL) were identified as independent prognosticators (Harrell’s C index, 0.682; integrated area under the curve, 0.653). Survival was clearly correlated with the risk groups (low, intermediate, and high, 14.0, 7.3, and 2.3 months, respectively; p < 0.001). The prognosis was also discriminative in the validation data set (median survival, 16.7, 7.5, and 1.9 months, respectively; p < 0.001). Chemotherapy did not offer any survival benefits for high-risk patients. Conclusion These proposed prognostic criteria for mBTC can facilitate accurate patient risk stratification and treatment-related decision-making.

Key words Biliary tract neoplasms, Prognosis, Scoring model, Survival, Validation, Drug therapy

+ Correspondence: + + + + + + +Hei-Cheul + + + + Jeung, + + +MD, + +PhD +++ + Pancreatobiliary + + + + + + +Cancer + + +Clinic, +++++++++ + Gangnam + + + + Severance + + + + Hospital, +++++++++++ + Yonsei + + +University + + + + College + + + of+ Medicine, ++++++++ ++++++++++++++++++++ 211 Eonju-ro, Gangnam-gu, Seoul + + + + + + + + + + + + + + 06273, + + +Korea +++ Tel: 82-2-2019-1242 ++++++++++++++++++++ + Fax: + +82-2-3463-3882 +++++++++++++++++ + E-mail: + + + [email protected] ++++++++++++++++ ++++++++++++++++++++ + Received + + + +November + + + + 14, + +2016 +++++++++ + Accepted + + + + January + + + 26, + +2017 ++++++++++ ++++++++++++++++++++ │ http://www.e-crt.org │

Copyright ⓒ 2017 by the Korean Cancer Association This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cancer Res Treat. 2017 Feb 6 [Epub ahead of print]

Introduction Biliary tract cancer (BTC) comprises a heterogeneous group of tumors arising from the biliary tree-lining cells. These tumors are classified further as gallbladder cancer, ampullary cancer, or cholangiocarcinoma. The latter type is divided phenotypically into intrahepatic and ductal cholangiocarcinomas (hilar and non-hilar) to emphasize the distinctions between these clinically distinct cancers [1,2]. BTC is a relatively rare malignancy in Western populations; approximately 2,500 cases of cholangiocarcinoma and 5,000 cases of gallbladder cancer occur annually in the USA, yielding an average annual incidence of one case per 100,000 people. In contrast, multiple independent studies have documented a steady increase in the worldwide incidence of cholangiocarcinoma [3,4], and BTC is relatively common in Asian countries, including Korea, Japan, China, and India [5], with an approximate incidence of six cases per 100,000 people [5,6]. In 2013, 5,283 patients were newly diagnosed with BTC and 3,783 related deaths were reported in Korea, where BTC is the sixth-leading cause of cancer mortality. Few treatment options are available for advanced BTC. Although a complete surgical resection remains the only curative treatment option, few patients are candidates for a potentially curative resection at the time of presentation. BTC is relatively chemo-/radio-resistant disease, and many patients are elderly and not healthy enough to tolerate aggressive treatment. Accordingly, these patients have dismal outcomes with a median survival of < 1 year [7]. Currently, combination chemotherapy with cisplatin and gemcitabine (Gem/Cis) is considered the standard first-line palliative treatment, regardless of the primary tumor location, which is based on the results from the ABC 02 trial [8]. On the other hand, the response rate to Gem/Cis is only 26%, with a median overall survival (OS) of ≤ 11.7 months. In other words, this regimen does not constitute an optimal treatment [8]. Therefore, it is essential to estimate the prognosis of a patient with metastatic BTC at the initial presentation, thus enabling patient individualization according to the risk and facilitating treatment optimization. To date, several reports have addressed the prognostic parameters associated with BTC. For example, lymph node metastasis is a typical poor prognosticator for resectable BTC, and laboratory analyses indicated that a high C-reactive protein level and neutrophil-to-lymphocyte ratio (NLR) are correlated with survival in patients with resectable cancer or those receiving systemic chemotherapy. Despite this, few prognostic studies have addressed metastatic disease at diagnosis [9-12]. Of these, the majority targeted predictions after chemotherapy (particularly Gem/Cis). Although Eastern Cooperative Oncology Group performance status (ECOG

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CANCER RESEARCH AND TREATMENT

PS), NLR or absolute neutrophil counts, hemoglobin, white blood cell (WBC) counts, primary tumor location, and the number of metastatic diseases have been proposed, the significance of these factors is unclear. Most of these parameters were identified using small sample sizes with inadequate statistical power, and their roles have rarely been confirmed independently. Another problem is the risk of bias when the points of cutoff for the continuous variables are random, leading to the use of different cutoff points across studies, which imped a direct comparison between studies. Ultimately, in a real-world clinical setting, the question of "which patients to treat" should be answered prior to "which regimen to use," and that the answer to the former question requires the proposal of prognosticators that could discriminate patients who might and might not benefit from treatment. Therefore, this paper presents the results of multivariate analysis of routinely evaluated clinico-laboratory parameters "at the time of initial diagnosis," to implement a scoring model that could effectively identify the risk groups, and validate the model in an independent dataset.

Materials and Methods 1. Patients From September 2006 to February 2015, 623 patients were diagnosed with metastatic BTC at Gangnam Severance Hospital, Seoul, Korea. The diagnosis was made via a surgical excision, tissue biopsy, or cytology. The inclusion criteria for further analysis were as follows: (1) age > 18 years, (2) histologically confirmed diagnosis of BTC, (3) metastatic disease at the time of diagnosis or systemic recurrence after a curative resection, and (4) available electronic medical records (including treatment information). The exclusion criteria were as follows: (1) widespread brain or leptomeningeal metastasis, (2) synchronous metastatic malignancies, (3) uncontrolled infection, active gastrointestinal bleeding, or other severe medical conditions, and (4) follow-up loss or transfer to another hospital prior to the decisions regarding treatment (including diagnosis only). Overall, 482 patients fulfilled these criteria and were further analyzed (Fig. 1). Our institutional review board approved this retrospective study and waived the requirement to obtain informed consent (3-2015-0318).

Hyung Soon Park, Prognostic Model of Metastatic BTC

Recurrent/metastatic biliary cancer (n=623) Biopsy not confirmed or not done (n=17) Pathology confirmed biliary cancer (n=606) Transferred to another center or follow-up refusal (n=96) Follow-up patients (n=510) Wide spread and symptomatic central nervous system metastasis (n=6) Synchronous metastatic cancers (n=13) Septic shock (n=4) Massive gastrointestinal bleeding (n=3) Hepatorenal syndrome (n=1) Hepatic encephalopathy (n=1) Eligible patients (n=482)

Fig. 1. Consort diagram of the enrolled patients.

2. Data collection The following baseline data were recorded at the initial presentation: age, sex, disease status (recurrent/metastatic), primary tumor location (gallbladder, ampullary, or cholangiocarcinoma–intrahepatic or ductal), metastatic disease sites (liver, peritoneal carcinomatosis, lung, bone, lymph nodes, or others), ECOG PS, and body mass index (BMI). The hematological and blood chemistry values included the WBC and platelet counts as well as the hemoglobin, serum protein, albumin, blood urea nitrogen (BUN), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, alkaline phosphatase (ALP), cholesterol, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA 19-9) levels. CEA was measured by a chemiluminescence immunoassay using UniCel DxI 800 immunoassay analyzer and CA 19-9 was measured by an electrochemiluminescence immunoassay using a Roche kit (Mannheim, Germany). The NLR was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count, and the plateletto-lymphocyte ratio (PLR) was derived from the ratio of the platelets to lymphocytes. Chest and abdomino-pelvic computed tomography (CT), radionuclide bone scan, and 18F-fluorodeoxyglucose positron emission tomography were performed to evaluate the distant metastasis. Radiological detection of peritoneal carcinomatosis relied on the following findings, which were evaluated by an independent radiologist(s): thickening and

nodular enhancement of peritoneal reflections, multiple soft tissue nodules, omental stranding and thickening (omental cake), small bowel mesentery stranding and distortion, and ascites, particularly if loculated. The presence of ascites was inferred from the evidence of malignant cell detection in a cytology examination or from suspicious fluid collection on CT or ultrasonography. 3. Dataset allocation For the prognostic scoring model, the patients were assigned randomly into the investigation or validation dataset at a ratio of 7:3 using the primary tumor location stratification factor. The scoring model derived from the investigational dataset was validated internally using three methods, Harrell’s C-index, integrated area under the curve (iAUC), and bootstrapping, followed by application to the independent validation dataset [13]. 4. Statistical analysis The hematological and blood chemistry values were initially recorded as continuous variables and later transformed to categorical variables according to the upper normal ranges (WBC, hemoglobin, platelet, serum protein, albumin, BUN, AST, ALT, bilirubin, ALP, and cholesterol) or the best cutoff point (CEA, CA 19-9, NLR, and PLR). The latter were determined using the Contal and O’Quigley method, which calCancer Res Treat. 2017 Feb 6 [Epub ahead of print]

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Table 1. Patient characteristics in the investigation and validation datasets Characteristic Age (yr) < 67 ≥ 67 Sex Male Female Disease status Recurrent Metastatic ECOG 0-2 3-4 Primary site Intrahepatic CC Hilar CC Non-hilar CC Gallbladder Ampullary BMI Complete blood count WBC count (/μL) Hemoglobin (g/dL) Platelet (×103/μL) NLR PLR Blood chemistry profile Protein (g/dL) Albumin (g/dL) BUN (mg/dL) AST (IU/L) ALT (IU/L) Bilirubin (mg/dL) ALP (IU/L) Cholesterol (mg/dL) Tumor marker CEA (ng/mL) CA 19-9 (U/mL) Palliative Tx No Gem/Cis Othersa)

Total (n=482)

Investigation dataset (n=340)

Validation dataset (n=142)

235 (48.8) 247 (51.2)

163 (47.9) 177 (52.1)

72 (50.7) 70 (49.3)

0.58

281 (58.3) 201 (41.7)

201 (59.1) 139 (40.9)

80 (56.3) 62 (43.7)

0.573

158 (32.8) 324 (67.2)

117 (34.4) 223 (65.6)

41 (28.9) 101 (71.1)

0.238

418 (86.7) 64 (13.3)

296 (87.1) 44 (12.9)

122 (85.9) 20 (14.1)

0.736

123 (25.5) 79 (16.4) 87 (18.0) 146 (30.3) 47 (9.8) 22.8 (20.6-25.0)

87 (25.6) 56 (16.5) 61 (17.9) 103 (30.3) 33 (9.7) 22.7 (20.4-24.9)

36 (25.4) 23 (16.2) 26 (18.3) 43 (30.3) 14 (9.9) 23.2 (21.0-25.1)

1.00

7,090 (5,525-9,615) 12.2 (11.1-13.4) 243 (189-319) 3.2 (2.0-5.8) 171 (122-251)

7,070 (5,555-9,650) 12.1 (11.1-13.4) 241 (186-314) 3.3 (2.0-5.7) 165 (120-234)

7,130 (5,328-9,563) 12.3 (11.1-13.4) 250 (200-326) 3.2 (2.1-6.1) 181 (123-275)

0.881 0.771 0.398 0.738

6.9 (6.5-7.4) 3.9 (3.4-4.3) 14.7 (11.5-19.2) 36 (22-75) 30 (17-74) 0.8 (0.5-3.4) 146 (89-321) 167 (134-198)

6.9 (6.5-7.4) 3.9 (3.4-4.3) 14.8 (11.6-19.3) 37 (23-81) 30 (17-72) 0.8 (0.5-3.6) 146 (88-321) 168 (134-194)

7.0 (6.5-7.4) 3.9 (3.4-4.3) 14.4 (11.3-18.9) 35 (22-68) 29 (18-76) 0.8 (0.5-2.7) 141 (91-312) 165 (132-202)

0.804 0.775 0.671 0.433 0.850 0.650 0.599 0.900

3.6 (1.9-16.0) 160 (26-1,472)

3.2 (1.8-15.1) 166 (25-1,587)

4.2 (2.4-18.9) 145 (27-1,298)

0.056 0.567

148 (30.7) 168 (34.9) 166 (34.4)

106 (31.2) 120 (35.3) 114 (33.5)

42 (29.6) 48 (33.8) 52 (36.6)

p-value

0.223

0.809

Values are presented as number (%) or median (interquartile range). The Mann-Whitney U test was used for comparisons of continuous values. ECOG, Eastern Cooperative Oncology Group; CC, cholangiocarcinoma; BMI, body mass index; WBC, white blood cell; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; BUN, blood urea nitrogen; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; CEA, carcinoembryonic antigen; CA 19-9, carbohydrate antigen 19-9; Tx, treatment; Gem/Cis, combination chemotherapy with cisplatin and gemcitabine. a)Fluorouracil or gemcitabine-based (excluding Gem/Cis) chemotherapy.

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culates the maximum hazard ratio (HR) based on log-rank statistics and estimates the best cutoff value [14]. The OS was defined as the time from the date of diagnosis to death from any cause. Survival curves were generated using the Kaplan-Meier method and compared using the logrank test. Univariate analysis was performed to determine the association of the following prognostic factors with OS: age, sex, disease status, ECOG PS, primary site, BMI, WBC count, hemoglobin, platelet count, serum protein, albumin, BUN, AST, ALT, bilirubin, ALP, cholesterol, CEA, CA 19-9, NLR, and PLR. Subsequently, stepwise multivariate analysis based on Cox’s proportional hazard model was performed

A

1.0

CEA

0.2

24 36 Time (mo)

48

NLR

0.6 0.4 0.2

0

12

24 36 Time (mo)

48

0.2

0

12

24 36 Time (mo)

60

48

60

D PLR < 150 (n=140) ≥ 150 (n=199)

0.8

p < 0.001

0

0.4

1.0

< 3 (n=153) ≥ 3 (n=186)

0.8

p < 0.001

0.6

0

60

C

1.0

Proportion of surviving


0.4



0.6

12

CA 19-9 < 120 (n=156) ≥ 120 (n=181)

0.8

p < 0.001

0

B

1.0

< 9 (n=231) ≥ 9 (n=104)

0.8

0

using the significant factors in univariate analysis. HRs, 95% confidence intervals (CIs), and chi-square scores were obtained for all regressions. Using the investigation dataset, a risk scoring model was devised by summing the rounded chi-square scores of independent prognostic factors identified in the multivariate analysis. Finally, patients were grouped into three risk groups according to the risk scores: low risk, intermediate risk, and high risk. The discriminatory power of this scoring system was estimated using Harrell’s c-index [15] and an iAUC. The latter was derived from time-dependent receiver operating characteristics curve analysis [16,17]. Finally, this

p < 0.001

0.6 0.4 0.2 0

0

12

24 36 Time (mo)

48

60

Fig. 2. Kaplan-Meier curves of overall survival for the investigation dataset (n=340): carcinoembryonic antigen (CEA) (A), carbohydrate antigen 19-9 (CA 19-9) (B), neutrophil-to-lymphocyte ratio (NLR) (C), and platelet-to-lymphocyte ratio (PLR) (D). Cancer Res Treat. 2017 Feb 6 [Epub ahead of print]

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Table 2. Univariate analysis of the overall survival Investigation dataset

Characteristic Age (yr) < 67 ≥ 67 Sex Male Female Disease status Recurrent Metastatic ECOG 0-2 3-4 Primary site Intrahepatic CC Hilar CC Non-hilar CC Gallbladder cancer Ampullary cancer BMI ≥ 18.5 < 18.5 WBC count (/μL) Normal (< 10,800) Abnormal (≥ 10,800) Hemoglobin (g/dL) Normal (≥ 12) Abnormal (< 12) Platelet (×103/μL) Normal (≥ 150) Abnormal (< 150) Protein (g/dL) Normal (≥ 6.9) Abnormal (< 6.9) Albumin (g/dL) Normal (≥ 3.4) Abnormal (< 3.4) BUN (mg/dL) Normal (< 23) Abnormal (≥ 23) AST (IU/L) Normal (< 30) Abnormal (≥ 30) ALT (IU/L) Normal (< 33) Abnormal (≥ 33) Bilirubin (mg/dL) Normal (< 1.2) Abnormal (≥ 1.2)

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HR

95% CI

1 1.311

1.043-1.649

1 0.926

0.735-1.167

1 1.584

1.24-2.023

1 4.499

3.205-6.316

1 0.802 0.91 0.94 0.514

0.563-1.144 0.643-1.289 0.692-1.276 0.331-0.8

1 2

1.332-3.002

1 1.779

1.327-2.383

1 1.391

1.107-1.748

1 1.375

0.955-1.98

1 1.337

1.064-1.681

1 2.216

1.698-2.892

1 1.401

0.988-1.988

1 1.187

0.94-1.501

1 1.034

0.823-1.299

1 1.099

0.867-1.394

Validation dataset p-value

HR

95% CI

0.02

1 1.231

0.865-1.754

1 1.014

0.713-1.443

1 1.462

0.989-2.161

1 5.276

3.102-8.972

1 0.834 0.592 0.895 0.519

0.479-1.451 0.349-1.004 0.56-1.431 0.255-1.057

1 2.225

1.111-4.459

1 3.177

2.014-5.012

1 1.254

0.881-1.784

1 0.935

0.535-1.634

1 1.635

1.149-2.325

1 1.699

1.132-2.552

1 1.956

1.178-3.25

1 1.279

0.895-1.826

1 1.239

0.869-1.768

1 1.277

0.88-1.853

0.515

< 0.001

< 0.001

0.045

0.001

< 0.001

0.005

0.086

0.013

< 0.001

0.058

0.151

0.773

0.435

p-value 0.249

0.937

0.057

< 0.001

0.187

0.024

< 0.001

0.209

0.814

0.006

0.011

0.01

0.176

0.237

0.198


Table 2. Continued Characteristic ALP (IU/L) Normal (< 123) Abnormal (≥ 123) Cholesterol (mg/dL) Normal (< 139) Abnormal (≥ 139) CEA (ng/mL)